Skip to main content

Advertisement

SpringerLink
Log in

A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Plug-in hybrid electric vehicles (PHEVs) have been the center of attention in recent years as they can be utilized to set up a bidirectional connection to a power grid for ancillary services procurement. By incorporating Vehicle to Grid (V2G), this paper proposes a real-time solution to a non-convex constrained unit commitment (UC) optimization problem considering V2G parking lots as dispersed generation units. V2G parking lots can be considered as virtual power plants that my decrease dependency to small expensive units in a UC problem. In this paper, firstly a probabilistic attendance model of PHEVs in a parking lot is investigated, while expected number of PHEVs as well as the equivalent generation capacity of the parking lot is obtained using a radial basis neural network. Secondly, a particular UC problem considering V2G parking lot is solved using GA-ANN as a hybrid heuristic method. A real-time estimation of PHEVs number in the V2G parking lot and real-time solution to UC–V2G problem associated with load variation makes this work distinguished, while the proposed method is applied to a standard IEEE 10-unit test system with promising results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

\(i\) :

Unit \(i\)

\(j\) :

jth prohibited operating zone

\(P_{i,t}\) :

Power output of unit \(i\) at hour \(t\)

\(u_{i,t}\) :

On or off status of unit \(i\) at hour \(t\)

\(\text{ SUC}_{i,t}\) :

Start-up cost of unit \(i\) at time \(t\)

\(\text{ SDC}_{i,t}\) :

Shut-down cost of unit \(i\) at time \(t\)

\(N\) :

Number of units

\(T\) :

Unit commitment horizon

\(C_\mathrm{V2G}\) :

Battery capacity of each BV

\(B_\mathrm{V2G}\) :

Number of BVs incorporated in the V2G aggregation at time \(t\)

\(a_i, b_i, c_i\) :

Fuel cost coefficients for unit \(i\)

\(\text{ HSC}_i\) :

Hot start-up cost

\(\text{ CSC}_i\) :

Cold start-up cost

\(T_{i,t}^D\) :

Minimum down-time of unit \(i\)

\(\text{ MD}_i^\mathrm{ON}\) :

Duration during which the \(i\)th unit is continuously on

\(\text{ CST}_i\) :

Cold start time of unit \(i\)

\(D_t\) :

Demand during hour \(t\)

\(L_t\) :

System losses at hour \(t\)

\(P_{i,t}^\mathrm{min}\) :

Minimum generation of unit \(i\)

\(P_{i,t}^\mathrm{max}\) :

Maximum generation of unit \(i\)

\(R_t\) :

Spinning reserve requirement at time \(t\)

\(\text{ RUR}_i\) :

Ramp up rate limit of unit \(i\)

\(\text{ RDR}_i\) :

Ramp down rate limit of unit \(i\)

\(P_{i,\,j}^\mathrm{Lower}\) :

Lower bound of the \(j\)th prohibited zone of unit \(i\)

\(P_{i,\,j}^\mathrm{Upper}\) :

Upper bound of the \(j\)th prohibited zone of unit \(i\)

\(\text{ PZ}_i\) :

Number of prohibited zones of unit \(i\)

\(\text{ MD}_i^\mathrm{ON}\) :

Duration during which the \(i\)th unit is continuously on

\(\text{ MD}_i^\mathrm{OFF}\) :

Duration during which the \(i\)th unit is continuously off

\(A\) :

The big positive number (assumed 1e+4)

chr:

Chromosomes counter

itr:

Iteration counter

References

  1. Yamin HY, El-Dwairi Q, Shahidehpour M (2007) A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty. Electr Power Energy Syst 29:609–616

    Google Scholar 

  2. Dieu VN, Ongsakul W (2008) Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network. EPSR 78:291–301

    Google Scholar 

  3. Lowery PG (1996) Generating unit commitment by dynamic programming. IEEE Trans Power Appar Syst 85(5):422–426

    Google Scholar 

  4. Senjyu T, Shimabukuro K, Uezato K, Funabashi T (2003) A fast technique for unit commitment problem by extended priority list. IEEE Trans Power Syst 18(2):882–888

    Article  Google Scholar 

  5. Ongsakul W, Petcharaks N (2004) Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Trans Power Syst 19(1): 620–628

    Google Scholar 

  6. Daneshi H, Choobbari AL, Shahidehpour M,Zuyi L (2008) Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limitsElectro/Information Technology. IEEE Conference on Electro/Info Technology, pp 187–192

  7. Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power Appar Syst PAS-102: 444–451

    Google Scholar 

  8. Wong SYW (1998) An enhanced simulated annealing approach to unit commitment. Electr Power Energy Syst 20:359–368

    Article  Google Scholar 

  9. Mantawy AH, Abel-Mogid YL, Selim SZ (1999) Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem. IEEE Trans Power Syst 14(3):829–836

    Article  Google Scholar 

  10. Saber AY, Senjyu T, Miyagi T, Urasaki N, Funabashi T (2006) Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing. IEEE Trans Power Syst 21(2):955–964

    Article  Google Scholar 

  11. Senjyu T, Saber AY, Miyagi T, Shimabukuro K, Urasaki N, Funabashi T (2005) Fast technique for unit commitment by genetic algorithm based on unit clustering. IEE Proc Gener Transm Distb 152(5):705–713

    Article  Google Scholar 

  12. Sun L, Zhang Y, Jiang C (2006) A matrix real-coded genetic algorithm to the unit commitment problem. Electr Power Syst Res 76:716–728

    Article  Google Scholar 

  13. Pourakbari-Kasmaei M, Rashidi-Nejad M, Abdollahi A (2009) A novel unit commitment technique considering prohibited operating zones. J Appl Sci 9(16):2962–2968

    Article  Google Scholar 

  14. Zhao B, Guo CX, Bai BR, Cao YJ (2006) An improved particle swarm optimization algorithm for unit commitment. Int J Electr Power Energy Syst 28:482–490

    Article  Google Scholar 

  15. Saber AY, Chakraborty S, AbdurRazzak SM, Senjyu T (2009) Optimization of economic load dispatch of higher order general cost polynomials and its sensitivity using modified particle swarm optimization. Electr Power Syst Res 79:98–106

    Article  Google Scholar 

  16. Shi L, Hao J, Zhou J, Xu G (2004) Ant colony optimisation algorithm with random perturbation behaviour to the problem of optimal unit commitment with probabilistic spinning reserve determination. Electr Power Syst Res 69:295–303

    Google Scholar 

  17. Simon SP, Padhy NP, Anand RS (2006) An ant colony system approach for unit commitment problem. Electr Power Energy Syst 28(5):315–323

    Article  Google Scholar 

  18. Ouyang Z, Shahidehpour SM (1992) A hybrid artificial neural network-dynamic programming approach to unit commitment. IEEE Trans Power Syst 7(February):236–242

    Article  Google Scholar 

  19. ZareianJahromi M, HosseiniBioki MM, Rashidinejad M, Fadaeinedjad R (2012) Solution to the unit commitment problem using artificial neural network. TJEECS (accepted for publication in 2012)

  20. Padhy NP (2004) Unit commitment—a bibliographical survey. IEEE Trans Power Syst 19(2):1196–1205

    Google Scholar 

  21. Daneshi H, Shahidehpour M, Afsharnia S, Naderian A, Rezaei A (2003) Application of fuzzy dynamic programming and neural network in generation scheduling. Proc IEEE Conf Power Tech 3(June):23–26

    Google Scholar 

  22. Cheng CP, Liu CW, Liu GC (2000) Unit commitment by lagrangian relaxation and genetic algorithms. IEEE Trans Power Syst 15(2):707–714

    Article  Google Scholar 

  23. Yamin HY, Shahidehpour SM (2003) Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets. Electr Power Syst Res 68:83–92

    Article  Google Scholar 

  24. Cheng CP, Liu CW, Liu CC (2002) Unit commitment by annealing genetic algorithms. Electr Power Energy Syst 24:149–158

    Article  Google Scholar 

  25. Saber AY, Venayagamoorthy GK (2009) Unit commitment with vehicle-to-grid using particle swarm optimization. IEEE Bucharest Power Tech Conference, Bucharest, pp 1–8

  26. Saber AY, Venayagamoorthy GK (2010) Intelligent unit commitment with vehicle-to-grid—a cost-emission optimization. J Power Sources 195:898–911

    Article  Google Scholar 

  27. Saber AY, Venayagamoorthy GK (2009) Optimization of vehicle-to-grid scheduling in constrained parking lots. PES General Meeting, pp 1–8

  28. Kempton W, Kubo T (2000) Electric-drive vehicles for peak power in Japan. Energy Policy 28(1):9–18

    Article  Google Scholar 

  29. Kempton W, Tomic J, Letendre S, Brooks A, Lipman T (2005) Vehicle to-grid power: battery, hybrid and fuel cell vehicles as resources for distributed electric power in California, Davis, CA. Institute of Transportation Studies, Report \(\#\) IUCD-ITS-RR 01-0

  30. Kempton W, Tomic J (2005) Vehicle to grid fundamentals: calculating capacity and net revenue. J Power Sources 1441(1):268–279

    Article  Google Scholar 

  31. Kempton W, Tomic J (2005) Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J Power Sources 144(1):280–294

    Article  Google Scholar 

  32. Williams BD, Kurani KS (2006) Estimating the early household market for light-duty hydrogen-fuel-cell vehicles and other “Mobile Energy innovations in California: a constraints analysis. J Power Sources 160(1):446–453

    Google Scholar 

  33. Tomic J, Kempton W (2007) Using fleets of electric-drive vehicles for grid support. J Power Sources 168(2):459–468

    Article  Google Scholar 

  34. Williams BD, Kurani KS (2007) Commercializing light-duty plugin/plug-out hydrogen-fuel-cell vehicles: mobile electricity—technologies and opportunities. J Power Sources 166(2):549–566

    Article  Google Scholar 

  35. Guille C (2009) A conceptual framework for the vehicle-to-grid implementation, MSc, Thesis. University of Illinois at Urbana-Champaign

  36. Guille C, Gross G (2009) A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy 37:4379–4390

    Article  Google Scholar 

  37. Hajek B (2006) An exploration of random processes for engineers, class notes for ECE 534, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, July 30, 2006

  38. City of Livermore (2006) Downtown parking study. Livermore, CA

  39. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks 2(2):302–309

    Article  Google Scholar 

  40. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley-IEEE, Hoboken

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Kerman Graduate University of Technology, Kerman, Iran

    M. M. Hosseini Bioki & M. Zareian Jahromi

  2. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    M. Rashidinejad

Authors
  1. M. M. Hosseini Bioki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Zareian Jahromi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Rashidinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Rashidinejad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bioki, M.M.H., Jahromi, M.Z. & Rashidinejad, M. A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G. Electr Eng 95, 341–355 (2013). https://doi.org/10.1007/s00202-012-0263-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-012-0263-5

Keywords

Search

Navigation